共查询到19条相似文献，搜索用时 78 毫秒
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层状介质参数反演的混合最优化法 总被引：8，自引：4，他引：4
波动方程反演是典型的非线性反问题,本文提出了用混合最优化法反演层状介质参数。混合最优化法将广义模拟退火与局部最优化方法结合,能较好地利用两者的优点,在本文中,局部最优化算法采用线性迭代算法。通过数值结果表明反演方法的正确有效性。 相似文献
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遗传优化神经网络方法在桥梁震害预测中的应用 总被引：5，自引：1，他引：4
本文将遗传算法与神经网络相结合,从而建立了一种高效的、实用的桥梁震害预测方法。根据遗传算法具有局部寻优的特点,为避免BP神经网络陷入局部极小值,本文将二者结合起来形成GA-BP混合算法,以GA优化神经网络的初始权值和阈值,对网络进行训练。在大量收集梁式桥震害资料的基础上,将此算法引入桥梁的震害预测中,并与传统的单独BP神经网络相比较,结果表明该方法能够有效、准确地对桥梁结构进行震害预测。 相似文献
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用随机启发式优化算法反演2003年云南大姚地震断层面参数的比较研究 总被引：1，自引：0，他引：1
基于余震分布确定主震断层面的数学模型,以确定断层面的走向和倾角参数进行计算,研究了遗传算法、模拟退火算法、差分演化算法、粒子群算法等4种最优化反演方法的反演效果和可靠性。结果显示,在涉及到的反演参数较少和非线性不太严重时,4种方法都有较好的表现,差分演化算法、粒子群算法速度快,精度高,遗传算法速度较慢,精度较低,模拟退火由于缺乏并行机制,速度较慢,精度高于遗传算法。余震在求出的断层附近分布图直观地反映出4种方法的效果和可靠性。 相似文献
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波阻抗反演的混合最优化算法 总被引：12，自引：5，他引：7
本文将广义快速模拟退火与线性方法相结合,利用两种方法的优点,提出一种混合最优化算法,算法以广义快速模拟退火为基础。当满足给定条件时,进行一定次数的线性迭代。我们将该算法用于波阻反演中,数值及实际资料实例表明方法的快速有效性。 相似文献
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快速模拟退火地震反演 总被引：10，自引：3，他引：10
讨论了用模拟退火方法进行地震资料的参数反演，利用快速的降温方式实现模拟退火反演，从而形成了快速模拟退火算法。模拟退火反演的优点是可以突破反演过程中局部最优的限制，获得全局最优解。因此，ＳＡ方法适于解决地震反演中的非凸性目标函数的最优化问题。 相似文献
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斜向探测是获取电离层状态信息的重要手段之一,对斜测电离图的反演可以得到电离层的相关结构参数.遗传算法是一种有效的并得到普遍应用的反演方法,该算法的求解不依赖于初值的选择,可以有效地减少反演问题解的非唯一性,但也存在“过早收敛”和局部搜索能力差等缺陷,从而导致反演精度下降,影响反演结果的可靠性.本文提出将基于模拟退火的混合遗传算法应用到斜测电离图的参数反演中,该算法不仅把握总体能力强,且具有较强的局部搜索能力,是遗传算法和模拟退火算法的优势互补.为了验证该算法反演结果的可靠性和稳定性,首先分别采用遗传算法、模拟退火算法和混合遗传算法对合成的电离图进行反演,反演参数包括临界频率,最大电子浓度和半厚度.通过对三种算法反演结果的对比,得出混合遗传算法的反演结果最接近真实值,需要的迭代次数也远远小于其他两种算法;通过改变种群大小和总迭代次数来判断参数值的改变对三种算法反演结果的影响,得出混合遗传算法有效地降低了参数的选取对反演结果的影响.然后用这三种反演算法对实测电离图进行反演,并将它们的反演结果与斜测链路中点的实际垂测数据进行比较,结果显示混合遗传算法84.62%的反演结果可以控制在误差范围之内,高于遗传算法（76.93%）和模拟退火算法（65.38%）.这些都表明了混合遗传算法的反演结果具有较强的可靠性,在反演的寻优能力和稳定性上要明显优于遗传算法和模拟退火算法,对实测电离层图的反演具有很强的借鉴意义和应用价值. 相似文献
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作为全局非线性优化的新方法之一的遗传算法,近年来已从生物工程流行到大地电磁测深资料解释中.然而,大地电磁反演问题具有不适定性,解的非唯一性.通过结合求解不适定问题的Tikhonov正则化方法,本文采用实数编码遗传算法求解大地电磁二维反演问题.此算法在构建目标函数时引入正则化的思想,利用遗传算法求解最优化问题.常规的基于局部线性化的最优化反演方法易使解陷入局部极小值,而且严重的依赖初始模型的选择.与传统线性化的迭代反演方法相比,实数编码遗传算法能够克服传统方法的不足且能获得更好的反演结果.通过对大地电磁测深理论模型进行计算,结果表明:该算法具有收敛速度快、解的精度高和避免出现早熟等优点,可用于大地电磁资料解释. 相似文献
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量子退火最优化方法的思想是基于模拟退火方法发展而来的，但利用的是量子跃迁隧道效应的机制，这和模拟退火基于热力学的退火原理是不一样的.本文首先概述了量子退火和模拟退火的区别所在；其次主要论述了量子退火在最优化方面的应用发展情况，比如伊辛自旋玻璃问题，旅行商问题，布尔可满足性问题等，讨论了具体测试问题中量子退火方法的应用情况以及和模拟退火方法的比较；随后对基于量子退火方法的地球物理反演原理和方法做了相应的阐述，讨论了不同参数比如温度、横向场的设置，比较了处于不同的横向场和势能状态的概率大小问题；最后就量子退火方法在地球物理反问体中的应用前景以及存在的问题做了简要概述. 相似文献
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In the past, graphical or computer methods were usually employed to determine the aquifer parameters of the observed data obtained from field pumping tests. Since we employed the computer methods to determine the aquifer parameters, an analytical aquifer model was required to estimate the predicted drawdown. Following this, the gradient‐type approach was used to solve the nonlinear least‐squares equations to obtain the aquifer parameters. This paper proposes a novel approach based on a drawdown model and a global optimization method of simulated annealing (SA) or a genetic algorithm (GA) to determine the best‐fit aquifer parameters for leaky aquifer systems. The aquifer parameters obtained from SA and the GA almost agree with those obtained from the extended Kalman filter and gradient‐type method. Moreover, all results indicate that the SA and GA are robust and yield consistent results when dealing with the parameter identification problems. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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建议一种SA和GA相结合的策略,较好地解决了GA收敛早熟及SA搜索效率较低的问题,提高了全局优化计算效率;在应用其依据面波频散曲线反演工程场地剪切波速时,利用简化剥层法提供较小的模型空间,取得了较好的反演效果. 相似文献
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We present a novel approach for optimizing reservoir operation through fuzzy programming and a hybrid evolution algorithm, i.e. genetic algorithm (GA) with simulated annealing (SA). In the analysis, objectives and constraints of reservoir operation are transformed by fuzzy programming for searching the optimal degree of satisfaction. In the hybrid search procedure, the GA provides a global search and the SA algorithm provides local search. This approach was investigated to search the optimizing operation scheme of Shihmen Reservoir in Taiwan. Monthly inflow data for three years reflecting different hydrological conditions and a consecutive 10‐year period were used. Comparisons were made with the existing M‐5 reservoir operation rules. The results demonstrate that: (1) fuzzy programming could effectively formulate the reservoir operation scheme into degree of satisfaction α among the users and constraints; (2) the hybrid GA‐SA performed much better than the current M‐5 operating rules. Analysis also found the hybrid GA‐SA conducts parallel analyses that increase the probability of finding an optimal solution while reducing computation time for reservoir operation. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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振幅随偏移距变化（AVO）反演是一个非线性的组合最优化过程，理论上可先将该非线性问题线性化，然后求解线性问题；或者直接利用非线性的模拟退火、遗传算法等方法求解.但无论哪种反演思路，实际中影响其精度的因素很多，因此分析AVO反演中的误差来源对提高反演精度和评价反演方法的可靠性非常重要.本文对能造成反演误差的主要因素，噪音、薄层调谐、地震数据处理中的误差、入射角范围等进行了分析，讨论了这些因素可能对反演结果造成的影响，发现在AVO反演过程中可以从优化参数选择和针对性处理方面来减小这些误差，提高反演精度. 相似文献
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A combined simulation–genetic algorithm (GA) optimization model is developed to determine optimal reservoir operational rule curves of the Nam Oon Reservoir and Irrigation Project in Thailand. The GA and simulation models operate in parallel over time with interactions through their solution procedure. A GA is selected as an optimization model, instead of traditional techniques, owing to its powerful and robust performance and simplicity in combining with a simulation technique. A GA is different from conventional optimization techniques in the way that it uses objective function information and does not require its derivatives, whereas in real‐world optimization problems the search space may include discontinuities and may often include a number of sub‐optimum peaks. This may cause difficulties for calculus‐based and enumerative schemes, but not in a GA. The simulation model is run to determine the net system benefit associated with state and control variables. The combined simulation–GA model is applied to determine the optimal upper and lower rule curves on a monthly basis for the Nam Oon Reservoir, Thailand. The objective function is maximum net system benefit subject to given constraints for three scenarios of cultivated areas. The monthly release is calculated by the simulation model in accordance with the given release policy, which depends on water demand. The optimal upper and lower rule curves are compared with the results of the HEC‐3 model (Reservoir System Analysis for Conservation model) calculated by the Royal Irrigation Department, Thailand, and those obtained using the standard operating policy. It was found that the optimal rule curves yield the maximum benefit and minimum damages caused by floods and water shortages. The combined simulation–GA model shows an excellent performance in terms of its optimization results and efficient computation. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model 总被引：1，自引：0，他引：1
With the popularity of complex hydrologic models, the time taken to run these models is increasing substantially. Comparing and evaluating the efficacy of different optimization algorithms for calibrating computationally intensive hydrologic models is becoming a nontrivial issue. In this study, five global optimization algorithms (genetic algorithms, shuffled complex evolution, particle swarm optimization, differential evolution, and artificial immune system) were tested for automatic parameter calibration of a complex hydrologic model, Soil and Water Assessment Tool (SWAT), in four watersheds. The results show that genetic algorithms (GA) outperform the other four algorithms given model evaluation numbers larger than 2000, while particle swarm optimization (PSO) can obtain better parameter solutions than other algorithms given fewer number of model runs (less than 2000). Given limited computational time, the PSO algorithm is preferred, while GA should be chosen given plenty of computational resources. When applying GA and PSO for parameter optimization of SWAT, small population size should be chosen. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems. 相似文献
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Improving the representation of soil moisture by using a semi‐analytical infiltration model 
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下载免费PDF全文 L. Brocca S. Camici F. Melone T. Moramarco J. Martínez‐Fernández J.‐F. Didon‐Lescot R. Morbidelli 《水文研究》2014,28(4):2103-2115
Soil moisture is widely recognized as a fundamental variable governing the mass and energy fluxes between the land surface and the atmosphere. In this study, the soil moisture modelling at sub‐daily timescale is addressed by using an accurate representation of the infiltration component. For that, the semi‐analytical infiltration model proposed by Corradini et al. (1997) has been incorporated into a soil water balance model to simulate the evolution in time of surface and profile soil moisture. The performances of this new soil moisture model [soil water balance module‐semi‐analytical (SWBM‐SA)] are compared with those of a precedent version [SWBM‐Green–Ampt (GA)] where the GA approach was employed. Their capability to reproduce in situ soil moisture observations at three sites in Italy, Spain and France is analysed. Hourly observations of quality‐checked rainfall, temperature and soil moisture data for a 2‐year period are used for testing the modelling approaches. Specifically, different configurations for the calibration and validation of the models are adopted by varying a single parameter, that is, the saturated hydraulic conductivity. Results indicate that both SWBMs are able to reproduce satisfactorily the hourly soil moisture temporal pattern for the three sites with root mean square errors lower than 0.024 m3/m3 both in the calibration and validation periods. For all sites, the SWBM‐SA model outperforms the SWBM‐GA with an average reduction of the root mean square error of ~20%. Specifically, the higher improvement is observed for the French site for which in situ observations are measured at 30 cm depth, and this is attributed to the capability of the SA infiltration model to simulate the time evolution of the whole soil moisture profile. The reasonable models performance coupled with the need to calibrate only a single parameter makes them useful tools for soil moisture simulation in different regions worldwide, also in scarcely gauged areas. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献